Key Technology of Cloud Service for the Aggregation of Satellite, Aerial and Terrestrial Multi-source Spatiotemporal Information

WenHao Ou (owhnet@163.com), Peng Luo (luopeng@sgdt.sgcc.com.cn), LinLin Liu (spplinda@163.com), Liang Shan (shanliang@sgec.sgcc.com.cn), JiaYi Chen (chenjiayi@sgec.sgcc.com.cn) and Zhenyu Wang (120184082@qq.com)
Additional contact information
WenHao Ou: State Grid Commercial Big Data Co., Ltd
Peng Luo: State Grid Commercial Big Data Co., Ltd
LinLin Liu: State Grid Commercial Big Data Co., Ltd
Liang Shan: State Grid Commercial Big Data Co., Ltd
JiaYi Chen: State Grid Commercial Big Data Co., Ltd
Zhenyu Wang: State Grid Commercial Big Data Co., Ltd

A chapter in LISS 2022, 2023, pp 393-403 from Springer

Abstract: Abstract In this paper, a spatiotemporal data model and efficient indexing method suitable for cloud service management is proposed, a unified and high-precision registering technology system for spatiotemporal reference of satellite, aerial and terrestrial multi-source sensing information is constructed and a multi-source spatiotemporal sensing information interaction interface standards and distribution mechanism scheme are developed to improve the accuracy and reliability of multi-source sensing data of power IOT, promote the sharing of power IOT data effectively, and provide the necessary conditions for the integrated application of multi-source spatiotemporal data in the core business scenarios of power grid.

Keywords: Cloud service; Aggregation; Multi-source spatiotemporal information (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-99-2625-1_31

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DOI: 10.1007/978-981-99-2625-1_31

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